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Galzin E, Roche L, Vlachomitrou A, Nempont O, Carolus H, Schmidt-Richberg A, Jin P, Rodrigues P, Klinder T, Richard JC, Tazarourte K, Douplat M, Sigal A, Bouscambert-Duchamp M, Si-Mohamed SA, Gouttard S, Mansuy A, Talbot F, Pialat JB, Rouvière O, Milot L, Cotton F, Douek P, Duclos A, Rabilloud M, Boussel L. Additional value of chest CT AI-based quantification of lung involvement in predicting death and ICU admission for COVID-19 patients. RESEARCH IN DIAGNOSTIC AND INTERVENTIONAL IMAGING 2022; 4:100018. [PMID: 37284031 PMCID: PMC9716289 DOI: 10.1016/j.redii.2022.100018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 11/15/2022] [Indexed: 12/03/2022]
Abstract
Objectives We evaluated the contribution of lung lesion quantification on chest CT using a clinical Artificial Intelligence (AI) software in predicting death and intensive care units (ICU) admission for COVID-19 patients. Methods For 349 patients with positive COVID-19-PCR test that underwent a chest CT scan at admittance or during hospitalization, we applied the AI for lung and lung lesion segmentation to obtain lesion volume (LV), and LV/Total Lung Volume (TLV) ratio. ROC analysis was used to extract the best CT criterion in predicting death and ICU admission. Two prognostic models using multivariate logistic regressions were constructed to predict each outcome and were compared using AUC values. The first model ("Clinical") was based on patients' characteristics and clinical symptoms only. The second model ("Clinical+LV/TLV") included also the best CT criterion. Results LV/TLV ratio demonstrated best performance for both outcomes; AUC of 67.8% (95% CI: 59.5 - 76.1) and 81.1% (95% CI: 75.7 - 86.5) respectively. Regarding death prediction, AUC values were 76.2% (95% CI: 69.9 - 82.6) and 79.9% (95%IC: 74.4 - 85.5) for the "Clinical" and the "Clinical+LV/TLV" models respectively, showing significant performance increase (+ 3.7%; p-value<0.001) when adding LV/TLV ratio. Similarly, for ICU admission prediction, AUC values were 74.9% (IC 95%: 69.2 - 80.6) and 84.8% (IC 95%: 80.4 - 89.2) respectively corresponding to significant performance increase (+ 10%: p-value<0.001). Conclusions Using a clinical AI software to quantify the COVID-19 lung involvement on chest CT, combined with clinical variables, allows better prediction of death and ICU admission.
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Affiliation(s)
- Eloise Galzin
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
| | - Laurent Roche
- Department of Biostatistics, Hospices Civils de Lyon, Lyon F-69003, France
- Université de Lyon, Lyon F-69000, France
- Laboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, CNRS, UMR5558, Equipe Biostatistique-Santé, Villeurbanne F-69622, France
| | - Anna Vlachomitrou
- Philips France, 33 rue de Verdun, CS 60 055, Suresnes Cedex 92156, France
| | - Olivier Nempont
- Philips France, 33 rue de Verdun, CS 60 055, Suresnes Cedex 92156, France
| | - Heike Carolus
- Philips Research, Röntgenstrasse 24-26, Hamburg D-22335, Germany
| | | | - Peng Jin
- Philips Medical Systems Nederland BV (Philips Healthcare), the Netherlands
| | - Pedro Rodrigues
- Philips Medical Systems Nederland BV (Philips Healthcare), the Netherlands
| | - Tobias Klinder
- Philips Research, Röntgenstrasse 24-26, Hamburg D-22335, Germany
| | - Jean-Christophe Richard
- Department of Critical Care Medicine, Hôpital De La Croix Rousse, Hospices Civils de Lyon, Lyon, France
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon U1294, France
| | - Karim Tazarourte
- Research on Healthcare Performance (RESHAPE), INSERM U1290, Université Claude Bernard Lyon 1, Lyon, France
- Emergency department and SAMU 69, Hospices civils de Lyon, France
| | - Marion Douplat
- Research on Healthcare Performance (RESHAPE), INSERM U1290, Université Claude Bernard Lyon 1, Lyon, France
- Emergency department and SAMU 69, Hospices civils de Lyon, France
| | - Alain Sigal
- Emergency department and SAMU 69, Hospices civils de Lyon, France
| | - Maude Bouscambert-Duchamp
- Laboratoire de Virologie, Institut des Agents Infectieux de Lyon, Centre National de Référence des virus respiratoires France Sud, Centre de Biologie et de Pathologie Nord, Hospices Civils de Lyon, Lyon F-69317, France
- Université de Lyon, Virpath, CIRI, INSERM U1111, CNRS UMR5308, ENS Lyon, Université Claude Bernard Lyon 1, Lyon F-69372, France
| | - Salim Aymeric Si-Mohamed
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon U1294, France
| | | | - Adeline Mansuy
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
| | - François Talbot
- Department of Information Technology, Hospices Civils de Lyon, Lyon, France
| | - Jean-Baptiste Pialat
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon U1294, France
| | - Olivier Rouvière
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- LabTAU INSERM U1032, Lyon, France
| | - Laurent Milot
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- LabTAU INSERM U1032, Lyon, France
| | - François Cotton
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon U1294, France
| | - Philippe Douek
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon U1294, France
| | - Antoine Duclos
- Research on Healthcare Performance (RESHAPE), INSERM U1290, Université Claude Bernard Lyon 1, Lyon, France
| | - Muriel Rabilloud
- Department of Biostatistics, Hospices Civils de Lyon, Lyon F-69003, France
- Université de Lyon, Lyon F-69000, France
- Laboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, CNRS, UMR5558, Equipe Biostatistique-Santé, Villeurbanne F-69622, France
| | - Loic Boussel
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon U1294, France
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Gomes R, Kamrowski C, Langlois J, Rozario P, Dircks I, Grottodden K, Martinez M, Tee WZ, Sargeant K, LaFleur C, Haley M. A Comprehensive Review of Machine Learning Used to Combat COVID-19. Diagnostics (Basel) 2022; 12:diagnostics12081853. [PMID: 36010204 PMCID: PMC9406981 DOI: 10.3390/diagnostics12081853] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/22/2022] [Accepted: 07/26/2022] [Indexed: 12/19/2022] Open
Abstract
Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.
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Affiliation(s)
- Rahul Gomes
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
- Correspondence:
| | - Connor Kamrowski
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Jordan Langlois
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Papia Rozario
- Department of Geography and Anthropology, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA;
| | - Ian Dircks
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Keegan Grottodden
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Matthew Martinez
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Wei Zhong Tee
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Kyle Sargeant
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Corbin LaFleur
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Mitchell Haley
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
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